Developments and applications of the OPTIMADE API for materials discovery, design, and data exchange

Evans, Matthew L.; Bergsma, Johan; Merkys, Andrius; Andersen, Casper W.; Andersson, Oskar B.; Beltrán, Daniel; Blokhin, Evgeny; Boland, Tara M.; Castañeda Balderas, Rubén; Choudhary, Kamal; Díaz Díaz, Alberto; Domínguez García, Rodrigo; Eckert, Hagen; Eimre, Kristjan; Fuentes Montero, María Elena; Krajewski, Adam M.; Mortensen, Jens Jørgen; Nápoles Duarte, José Manuel; Pietryga, Jacob; Qi, Ji; Trejo Carrillo, Felipe de Jesús; Vaitkus, Antanas; Yu, Jusong; Zettel, Adam; de Castro, Pedro Baptista; Carlsson, Johan; Cerqueira, Tiago F. T.; Divilov, Simon; Hajiyani, Hamidreza; Hanke, Felix; Jose, Kevin; Oses, Corey; Riebesell, Janosh; Schmidt, Jonathan; Winston, Donald; Xie, Christen; Yang, Xiaoyu; Bonella, Sara; Botti, Silvana; Curtarolo, Stefano; Draxl, Claudia; Fuentes Cobas, Luis Edmundo; Hospital, Adam; Liu, Zi-Kui; Marques, Miguel A. L.; Marzari, Nicola; Morris, Andrew J.; Ong, Shyue Ping; Orozco, Modesto; Persson, Kristin A.; Thygesen, Kristian S.; Wolverton, Chris; Scheidgen, Markus; Toher, Cormac; Conduit, Gareth J.; Pizzi, Giovanni; Gražulis, Saulius; Rignanese, Gian-Marco; Armiento, Rickard

doi:10.1039/d4dd00039k

Cited by 3 publications

(1 citation statement)

References 132 publications

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“…In order for this field to progress, the amount of publicly available and unique high-throughput DFT data must increase, which can be one of the most effective routes to improving the accuracy of these machine learning models. Collaborative efforts such as the JARVIS-Leaderboard [94], which allows users to view and contribute materials science data and machine learning models and assess accuracy/ fidelity (while enhancing transparency and reproducibility) and OPTIMADE (Open Databases Integration for Materials Design) [95], which allows for the easy and interoperable access to information across different materials databases in a uniform format can play an important role in granting access to relevant data and metrics with the goal of advancing the field of materials design.…”

Section: Future Perspectivesmentioning

confidence: 99%

Data-driven design of high pressure hydride superconductors using DFT and deep learning

Wines,

Choudhary

2024

Mater. Futures

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The observation of superconductivity in hydride-based materials under ultrahigh pressures (for example, H3S and LaH10) has fueled the interest in a more data-driven approach to discovering new high-pressure hydride superconductors. In this work, we performed density functional theory (DFT) calculations to predict the critical temperature (Tc) of over 900 hydride materials under a pressure range of (0 to 500) GPa, where we found 122 dynamically stable structures with a Tc above MgB2 (39 K). To accelerate screening, we trained a graph neural network (GNN) model to predict Tc and demonstrated that a universal machine learned force-field can be used to relax hydride structures under arbitrary pressures, with significantly reduced cost. By combining DFT and GNNs, we can establish a more complete map of hydrides under pressure.

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Section: Future Perspectivesmentioning

confidence: 99%

Data-driven design of high pressure hydride superconductors using DFT and deep learning

Wines,

Choudhary

2024

Mater. Futures

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Jupyter widgets and extensions for education and research in computational physics and chemistry

Du,

Baird,

Eimre

et al. 2024

Computer Physics Communications

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Optical materials discovery and design with federated databases and machine learning

Trinquet,

Evans,

Hargreaves

et al. 2024

Faraday Discuss.

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Developments and applications of the OPTIMADE API for materials discovery, design, and data exchange

Abstract: The Open Databases Integration for Materials Design (OPTIMADE) application programming interface (API) empowers users with holistic access to a growing federation of databases, enhancing the accessibility and discoverability of materials...

Cited by 3 publications

References 132 publications

Data-driven design of high pressure hydride superconductors using DFT and deep learning

Data-driven design of high pressure hydride superconductors using DFT and deep learning

Jupyter widgets and extensions for education and research in computational physics and chemistry

Optical materials discovery and design with federated databases and machine learning

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